3 research outputs found

    Learning health systems need to bridge the ‘two cultures’ of clinical informatics and data science

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    Background UK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational 'Big Data'. Learning health systems require not only data, but feedback loops of knowledge into changed practice. This depends on knowledge management and application, which in turn depends upon effective system design and implementation. Biomedical informatics is the interdisciplinary field at the intersection of health science, social science and information science and technology that spans this entire scope. Issues In the UK, the separate worlds of health data science (bioinformatics, 'Big Data') and effective healthcare system design and implementation (clinical informatics, 'Digital Health') have operated as 'two cultures'. Much National Health Service and social care data is of very poor quality. Substantial research funding is wasted on 'data cleansing' or by producing very weak evidence. There is not yet a sufficiently powerful professional community or evidence base of best practice to influence the practitioner community or the digital health industry. Recommendation The UK needs increased clinical informatics research and education capacity and capability at much greater scale and ambition to be able to meet policy expectations, address the fundamental gaps in the discipline's evidence base and mitigate the absence of regulation. Independent evaluation of digital health interventions should be the norm, not the exception. Conclusions Policy makers and research funders need to acknowledge the existing gap between the 'two cultures' and recognise that the full social and economic benefits of digital health and data science can only be realised by accepting the interdisciplinary nature of biomedical informatics and supporting a significant expansion of clinical informatics capacity and capability.</p

    Use of QuEChERS as a manual and automated high-throughput protocol for investigating environmental matrices

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    Environmental pollution has strong links to adverse human health outcomes with risks of pollution through production, use, ineffective wastewater (WW) remediation, and/or leachate from landfill. 'Fit-for-purpose' monitoring approaches are critical for better pollution control and mitigation of harm, with current sample preparation methods for complex environmental matrices typically time-consuming and labour intensive, unsuitable for high-throughput screening. This study has shown that a modified 'Quick Easy Cheap Effective Rugged and Safe' (QuEChERS) sample preparation is a viable alternative for selected environmental matrices required for pollution monitoring (e.g. WW effluent, treated sludge cake and homogenised biota tissue). As a manual approach, reduced extraction times (hours to ∼20 min/sample) with largely reproducible (albeit lower) recoveries of a range of pharmaceuticals and biocidal surfactants have been reported. Its application has shown clear differentiation of matrices via chemometrics, and the measurement of pollutants of interest to the UK WW industry at concentrations significantly above suggested instrument detection limits (IDL) for sludge, indicating insufficient removal and/or bioaccumulation during WW treatment. Furthermore, new pollutant candidates of emerging concern were identified - these included detergents, polymers and pharmaceuticals, with quaternary ammonium compound (QAC) biocides observed at 2.3-70.4 mg/kg, and above levels associated with priority substances for environmental quality regulation (EQSD). Finally, the QuEChERS protocol was adapted to function as a fully automated workflow, further reducing the resource to complete both the preparation and analysis to 62%), and when applied to a largely un-investigated clay matrix, acceptable recovery (88.0-131.1%) and precision (≤10.3% RSD) for the tested pharmaceuticals and biocides was maintained. Therefore, this preliminary study has shown the successful application of a high-throughput QuEChERS protocol across a range of environmental solids for potential deployment in a regulated laboratory

    Technological capabilities to assess digital excellence in hospitals in high performing healthcare systems::an international eDelphi exercise

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    Background: Hospitals worldwide are developing ambitious digital transformation programs as part of broader efforts to create digitally advanced health care systems. However, there is as yet no consensus on how best to characterize and assess digital excellence in hospitals. Objective: Our aim was to develop an international agreement on a defined set of technological capabilities to assess digital excellence in hospitals. Methods: We conducted a two-stage international modified electronic Delphi (eDelphi) consensus-building exercise, which included a qualitative analysis of free-text responses. In total, 31 international health informatics experts participated, representing clinical, academic, public, and vendor organizations. Results: We identified 35 technological capabilities that indicate digital excellence in hospitals. These are divided into two categories: (a) capabilities within a hospital (n=20) and (b) capabilities enabling communication with other parts of the health and social care system, and with patients and carers (n=15). The analysis of free-text responses pointed to the importance of nontechnological aspects of digitally enabled change, including social and organizational factors. Examples included an institutional culture characterized by a willingness to transform established ways of working and openness to risk-taking. The availability of a range of skills within digitization teams, including technological, project management and business expertise, and availability of resources to support hospital staff, were also highlighted. Conclusions: We have identified a set of criteria for assessing digital excellence in hospitals. Our findings highlight the need to broaden the focus from technical functionalities to wider digital transformation capabilities
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